Flexible dependence modeling of operational risk losses and its impact on total capital requirements

Operational risk data, when available, are usually scarce, heavy-tailed and possibly dependent. In this work, we introduce a model that captures such real-world characteristics and explicitly deals with heterogeneous pairwise and tail dependence of losses. By considering flexible families of copulas, we can easily move beyond modeling bivariate dependence among losses and estimate the total risk capital for the seven- and eight-dimensional distributions of event types and business lines. Using real-world data, we then evaluate the impact of realistic dependence modeling on estimating the total regulatory capital, which turns out to be up to 38% smaller than what the standard Basel approach would prescrib

Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine

Flexible multivariate distributions are needed in many areas. The popular multivariate Gaussian distribution is however very restrictive and cannot account for features like asymmetry and heavy tails. Therefore dependence modeling using copulas is nowadays very common to account for such patterns. The use of copulas is however challenging in higher dimensions, where standard multivariate copulas suffer from rather inflexible structures. Vine copulas overcome such limitations and are able to model complex dependency patterns by benefiting from the rich variety of bivariate copulas as building blocks. This article presents the R package CDVine which provides functions and tools for statistical inference of canonical vine (C-vine) and D-vine copulas. It contains tools for bivariate exploratory data analysis and for bivariate copula selection as well as for selection of pair-copula families in a vine. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and graphical methods are also included

Proof-of-concept of a novel scalable magnetic bead-based cell separation technology

Advanced Therapy Medicinal Products (ATMPs) are gaining great interest for the treatment of severe, currently consider incurable, diseases. Therapies based on stem cells have an enormous potential in applications such as cardiac cells and neurons to name a few. However, the production of these cell systems is expensive, complex and lack, nowadays, scalability both for their cultivation and the purification. The lack of scalability is a major bottleneck to bring these therapies to patient at commercial scale. Magnetic beads are well-established for sorting of cells, e.g. magnetic activated cell sorting. However, today´s systems size is still limited in terms of scale-up potential. We have developed a new scalable separation process based on the magnetic bead MAG for the isolation of receptor positive cell subpopulations. We have previously published that our new magnetic bead system MAG is extremely gentle towards cells1 and can easily be scaled up at pilot-scale for the separation of monoclonal antibody from a cell suspension2. In the present study, this magnetic bead system has been further developed for cell separation. In a model system with a mixture of hMSC and HER2+ SK BR3 cells (20:80), a proof-of-concept was demonstrated showing exceptional elimination of the HER2+ cells. Different ligand densities were evaluated, showing that the largest cell removals were achieved with the lowest ligand densities. Furthermore, in a study of mechanical and chemical stress conditions, the MAG separation system showed robustness of sorting performances. From our previous knowledge about the scalability of the MAG magnetic beads separation, this provides promising potential for the production of therapeutic stem cells at larger scale. 1. Brechmann, N. A.; Schwarz, H.; Eriksson, P.-O.; Eriksson, K.; Shokri, A.; Chotteau, V., Antibody capture process based on magnetic beads from very high cell density suspension. Biotechnology and Bioengineering 2021, n/a, (n/a). 2. Brechmann, N. A.; Eriksson, P.-O.; Eriksson, K.; Oscarsson, S.; Buijs, J.; Shokri, A.; Hjälm, G.; Chotteau, V., Pilot-scale process for magnetic bead purification of antibodies directly from non-clarified CHO cell culture. Biotechnology Progress 2019, 35, (3), e2775

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.DFG, 54371073, SFB/TRR 62: Eine Companion-Technologie für kognitive technische System

Identification and Characterization of Protein Phosphatase 4 Regulatory Subunit 1 (PP4R1) as a Suppressor of NF-kappaB in T Lymphocytes and T Cell Lymphomas

The transcription factor nuclear factor-kappaB (NF-kappaB) plays a key role in the immune system by controlling lymphocyte survival and activation. Conversely, aberrant NF-kappaB activity has been implicated in several lymphoid malignancies and contributes to a variety of autoimmune disorders. While multiple kinases and phosphorylated target proteins that induce NF-kappaB activity have been identified, the molecular machinery involved in the termination of antigen receptor-mediated NF-kappaB activation is only partially understood. Since signal transduction from activated receptors to NF-kappaB largely relies on phosphorylation events, phosphatases are expected to play a major role in the modulation and termination of NF-kappaB activity. Therefore, the current study aimed at systematically defining phosphatases that are involved in T cell receptor (TCR)-induced NF-kappaB signaling. To this end, an RNA interference (RNAi) genetic screen has been adopted based on a novel NF-kappaB-dependent reporter system. Using this approach, several NF-kappaB-modulating phosphatases were identified among which the protein phosphatase 4 regulatory subunit 1 (PP4R1) was confirmed as a central negative regulator of NF-kappaB activity in T lymphocytes. PP4R1 expression is strongly upregulated in primary human T lymphocytes upon activation. PP4R1 specifically binds to the inhibitor of NF-kappaB kinase alpha (IKKalpha) and the catalytic subunit PP4c, thereby directing PP4c phosphatase activity to dephosphorylate and inactivate the IKK complex. Accordingly, PP4R1 silencing causes sustained and increased IKK activity and T cell hyperactivation as reflected by enhanced induction of NF-kappaB target genes and secretion of cytokines. Conversely, PP4R1 overexpression significantly impairs NF-kappaB activation upon TCR stimulation, but does not affect AP-1 signaling. Furthermore, PP4R1 was found to be downregulated in a subset of malignant T lymphocytes derived from patients with Sézary syndrome, a severe form of cutaneous T cell lymphoma (CTCL). PP4R1 deficiency causes constitutive IKK/NF-kappaB signaling and is required for survival of NF-kappaB-addicted CTCL cells. In summary, the present work identified PP4R1 as a central gatekeeper of IKK activity and as a suppressor of T cell activation and lymphoma survival. These findings expand our current knowledge of NF-kappaB signal transduction and contribute to a more precise molecular understanding of NF-kappaB regulation in health and disease

Proof-of-concept of a novel scalable magnetic bead-based cell separation technology

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